System and method for brand monitoring and trend analysis based on deep-content-classification

ABSTRACT

A system and method for matching an advertisement item to a multimedia content element based on sentiments. The method comprises: extracting at least one multimedia content element from a web-page requested for display on a user node; generating a signature for each of the at least one multimedia content element, wherein each signature represents a concept, wherein each concept is an abstract description of one of the at least one multimedia content element; correlating the concepts of the generated signatures to determine a context of the at least one multimedia content element, wherein the context indicates at least a brand sentiment; searching for at least one advertisement item based on the signatures and the context; and causing a display of the at least one advertisement item within a display area of the web-page.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/874,115 filed on Apr. 30, 2013, now allowed, which claims the benefitof U.S. provisional application No. 61/789,576 filed on Mar. 15, 2013and is a continuation-in-part (CIP) of U.S. patent application Ser. No.13/624,397 filed on Sep. 21, 2012, now U.S. Pat. No. 9,191,626. The Ser.No. 13/624,397 application is a CIP of:

(a) U.S. patent application Ser. No. 13/344,400 filed on Jan. 5, 2012,now U.S. Pat. No. 8,959,037, which is a continuation of U.S. patentapplication Ser. No. 12/434,221 filed on May 1, 2009, now U.S. Pat. No.8,112,376. The Ser. No. 12/434,221 Application is a CIP of thebelow-referenced U.S. patent application Ser. Nos. 12/195,863 and12/084,150. The Ser. No. 13/344,400 Application is also a CIP of thebelow-referenced U.S. patent application Ser. Nos. 12/195,863 and12/084,150;

(b) U.S. patent application Ser. No. 12/195,863, filed on Aug. 21, 2008,now U.S. Pat. No. 8,326,775, which claims priority under 35 USC 119 fromIsraeli Application No. 185414, filed on Aug. 21, 2007, and which isalso a continuation-in-part of the below-referenced U.S. patentapplication Ser. No. 12/084,150; and,

(c) U.S. patent application Ser. No. 12/084,150 having a filing date ofApr. 7, 2009, now U.S. Pat. No. 8,655,801, which is the National Stageof International Application No. PCT/IL2006/001235, filed on Oct. 26,2006, which claims foreign priority from Israeli Application No. 171577filed on Oct. 26, 2005 and Israeli Application No. 173409 filed on 29Jan. 2006.

All of the applications referenced above are herein incorporated byreference for all that they contain.

TECHNICAL FIELD

The present invention relates generally to the analysis of multimediacontent displayed in web-pages, and more specifically to a system foridentifying trends, and analysis of multimedia content associated brandsdisplayed in web-pages.

BACKGROUND

The World Wide Web (WWW) contains a variety of multimedia content whichis commonly used by advertisers in order to promote different brands.Such advertisers commonly use a variety of web platforms while trying totrack the performance of their brands. The web platforms include, forexample, social networks, banners in popular websites, advertisements invideo clips, and so on.

As many web platforms are used as means for advertising, it has becomemore difficult to track the performance and efficiency of each webplatform with regard to an advertisement or a practical brand.Furthermore, as the brands' sentiment cannot be determined in real-timeit is highly difficult to track the trendiness of a brand's sentiment,for example, the tracking of the users' likes or dislikes of a practicalbrand at any given time.

It would be therefore advantageous to provide a solution for trendanalysis of brands advertised through the various the web platforms.

SUMMARY

Certain embodiments disclosed herein include a method for matching anadvertisement item to a multimedia content element based on sentiments.The method comprises: extracting at least one multimedia content elementfrom a web-page requested for display on a user node; generating asignature for each of the at least one multimedia content element,wherein each signature represents a concept, wherein each concept is anabstract description of one of the at least one multimedia contentelement; correlating the concepts of the generated signatures todetermine a context of the at least one multimedia content element,wherein the context indicates at least a brand sentiment; searching forat least one advertisement item based on the signatures and the context;and causing a display of the at least one advertisement item within adisplay area of the web-page.

Certain embodiments disclosed herein also include a system for matchingan advertisement item to a multimedia content element based onsentiments. The system comprises a processing unit; and a memory, thememory containing instructions that, when executed by the processingunit, configures the system to: extract at least one multimedia contentelement from a web-page requested for display on a user node; generate asignature for each of the at least one multimedia content element,wherein each signature represents a concept, wherein each concept is anabstract description of one of the at least one multimedia contentelement; correlate the concepts of the generated signatures to determinea context of the at least one multimedia content element, wherein thecontext indicates at least a brand sentiment; search for at least oneadvertisement item based on the signatures and the context; and cause adisplay of the at least one advertisement item within a display area ofthe web-page.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed subject matter is particularly pointed out and distinctlyclaimed in the claims at the conclusion of the specification. Theforegoing and other objects, features, and advantages of the inventionwill be apparent from the following detailed description taken inconjunction with the accompanying drawings.

FIG. 1 is a schematic block diagram of a network system utilized todescribe the various embodiments disclosed herein;

FIG. 2 is a flowchart describing the process of matching anadvertisement to multimedia content displayed on a web-page;

FIG. 3 is a block diagram depicting the basic flow of information in thesignature generator system;

FIG. 4 is a diagram illustrating the flow of patches generation,response vector generation, and signature generation in a large-scalespeech-to-text system; and

FIG. 5 is a flowchart describing a method for determining a sentimentwith respect to a brand according to one embodiment.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedinventions. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

Certain exemplary embodiments disclosed herein allow analyzing one ormore multimedia content elements to identify the existence of a brandadvertised or otherwise displayed through a plurality of web platforms.For each identified multimedia content element at least one signature isgenerated. The signatures are utilized to determine a brand's sentiment.The determined sentiment may be a positive, a natural or negativesentiment. The determined brand's sentiment is stored in a database. Thedetermination of the brand's sentiment may be based in part, on anidentification of a volume appearance of the brand or one or more itemsrelated to the brand within the multimedia content, a context in whichthe brand is appeared, and so on. The trendiness of a brand's sentimentis determined respective of its previously determined sentiments asstored in the database.

FIG. 1 shows an exemplary and non-limiting schematic diagram of anetwork system 100 utilized to describe the various embodimentsdisclosed herein. A network 110 is used to communicate between differentparts of the system 100. The network 110 may be the Internet, theworld-wide-web (WWW), a local area network (LAN), a wide area network(WAN), a metro area network (MAN), and other networks capable ofenabling communication between the elements of the system 100.

Further connected to the network 110 are one or more clientapplications, such as web browsers (WB) 120-1 through 120-n(collectively referred to hereinafter as web browsers 120 orindividually as a web browser 120). A web browser 120 is executed over acomputing device including, for example, a personal computer (PC), apersonal digital assistant (PDA), a mobile phone, a smart phone, atablet computer, and other kinds of wired and mobile appliances,equipped with browsing, viewing, listening, filtering, and managingcapabilities etc., that are enabled as further discussed herein below.

The system 100 also includes a plurality of servers 150-1 through 150-m(collectively referred to hereinafter as servers 150 or individually asserver 150) being connected to the network 110. Each of the servers 150may be, for example, a web server, an application server, a publisherserver, an ad-serving system, a data repository, a database, and thelike. Also connected to the network 110 is a data warehouse 160 thatstores multimedia content elements, clusters of multimedia contentelements, and the context determined for a web page as identified by itsURL. In the embodiment illustrated in FIG. 1, a brand-analyzer 130communicates with the data warehouse 160 through the network 110. Inother non-limiting configurations, the brand-analyzer 130 is directlyconnected to the data warehouse 160.

The various embodiments disclosed herein are realized using thebrand-analyzer 130 and a signature generator system (SGS) 140. The SGS140 may be connected to the brand-analyzer 130 directly or through thenetwork 110. The brand-analyzer 130 is enabled to receive and servemultimedia content elements and causes the SGS 140 to generate asignature respective of the multimedia content elements. The process forgenerating the signatures for the multimedia content elements isexplained in greater detail herein below with respect to FIGS. 3 and 4.It should be noted that each of the brand-analyzer 130 and the SGS 140,typically comprises a processing unit, such as a processor (not shown)that is coupled to a memory. The memory contains instructions that canbe executed by the processing unit. The transaction of thebrand-analyzer 130 also includes an interface (not shown) to the network110. According to one embodiment, the brand- analyzer 130 is a serversystem.

According to the disclosed embodiments, the brand-analyzer 130 isconfigured to receive at least a URL of a web page hosted in the server150 and accessed by a web browser 120. The brand-analyzer 130 is furtherconfigured to analyze the multimedia content elements contained in theweb page to determine their context, thereby ascertaining the context ofthe web page. This is performed based on at least one signaturegenerated for each multimedia content element. It should be noted thatthe context of an individual multimedia content element or a group ofelements is extracted from the web page, received from a user of a webbrowser 120 (e.g., uploaded video clip), or retrieved from the datawarehouse 160.

According to the embodiments disclosed herein, a user visits a web-pageusing a web browser 120. When the web-page is uploaded on the user's webbrowser 120, a request is sent to the brand-analyzer 130 to analyze themultimedia content elements contained in the web-page. The request toanalyze the multimedia content elements can be generated and sent by ascript executed in the web-page, an agent installed in the web-browser,or by one of the servers 150 (e.g., a web server or a publisher server)when requested to upload one or more advertisements to the web-page. Therequest to analyze the multimedia content may include a URL of theweb-page or a copy of the web-page. In one embodiment, the request mayinclude multimedia content elements extracted from the web-page. Amultimedia content element may include, for example, an image, agraphic, a video stream, a video clip, an audio stream, an audio clip, avideo frame, a photograph, an image of signals (e.g., spectrograms,phasograms, scalograms, etc.), and/or combinations thereof and portionsthereof.

The brand-analyzer 130 analyzes the multimedia content elements in theweb-page to determine if they are associated with a particular brand. Asan example, if the web page contains an image of a bar, the image isanalyzed to determine if it contains a logo of a brand-name lager. Thelogo may appear, for example, on beer glasses or on a signboard. Then atleast one signature is generated by means of the SGS 140 for theidentified brand. The generated signature(s) may be robust to noise anddistortion as discussed below.

Then, using the generated signature(s) the brand-analyzer 130 searchesfor multimedia content elements containing the identified brand. Thesearch may be performed by crawling the plurality of servers 150 and/orthe data warehouse 160. In one embodiment, for each multimedia contentelement encountered during the search, at least one signature isgenerated which is compared to the signature of a multimedia elementidentifying the brand. If the signatures are substantially then theencountered multimedia content element is determined to be related tothe brand. For example, a predefined number of least significant bitsshould be the same in the compared signatures.

The at least one signature generated for any multimedia content elementthat relates to the brand represents a concept. A concept is an abstractdescription of the content to which the signature was generated. As anexample, a concept of the signature generated for a picture showing abouquet of red roses is “flowers”. As another example, a concept of thesignature generated for a picture showing a bouquet of wilted roses is“wilted flowers”. According to these examples a correlation between theconcepts can be achieved by probabilistic models to determine that theconcept of “Flowers” has a positive connotation in comparison to theconcept “wilted flowers”. Moreover, the correlation between concepts canbe achieved by identifying a ratio between signatures' sizes, a spatiallocation of each signature, and so on using the probabilistic models.

As an example, according to an image analysis, a logo of a brand-namelager is identified on a beer glass in a bar and a tequila bottle signis identified on a signboard at the entrance to the bar. Signatures aregenerated for the brand-name lager and the tequila sign. Because agenerated signature represents a concept and is generated for amultimedia content element, the signature can also be utilized todetermine if somehow the brand-name lager is liked or disliked. Suchdetermination is possible, for example, respective of the identificationof the ratio between the signatures' sizes (the brand-name lagercompared with the tequila sign) and the spatial location of thebrand-name lager compared with the tequila sign. According to thisexample, the brand of the tequila sign is probably more significant thanthe brand-name lager because the size of the sign and the signboard'slocation in the image are more significant than the lager's logopresented on the beer glass. It should be noted that identifying, forexample, the ratio of signatures' sizes may also indicate the ratiobetween the sizes of their respective multimedia elements.

The brand-analyzer 130 then analyzes the signatures to correlate betweentheir respective concepts and to determine a context of such acorrelation. The context represents the brand sentiment. According toone embodiment, the determined sentiment may be a positive, natural, ornegative sentiment. Because a context is the correlation between aplurality of concepts, a strong context is determined when there aremore concepts than a predefined threshold which satisfy the samepredefined condition.

An exemplary technique for determining the context from signaturesgenerated for multimedia content elements is described in detail in U.S.patent application Ser. No. 13/770,603, filed Feb. 19, 2013, which isassigned to the common assignee, and is hereby incorporated by referencefor all the useful information it contains.

Following is a non-limiting example for the operation of thebrand-analyzer 130. An input image including a logo of the brand Gucciis received. A first signature is generated for the “Gucci” logo. Then,the brand-analyzer 130 crawls through one or more web sources in orderto identify mentions of Gucci. Examples for such mentions includepictures of models wearing a Gucci Jacket, and/or fans commenting onsuch pictures through social media websites. The brand-analyzer 130 thengenerates at least one signature for any mention of the brand. Forinstance, the crawling process encountered a picture of model Kate Mosswearing Gucci sunglasses and a comment made by a fan of Kate Moss to apicture. A signature is generated for each such mention. That is, afirst signature is generated for the Gucci logo (representing a firstconcept), a second signature is generated of Kate Moss's picture(representing a second concept), and a third signature is generated ofthe fan's comment (representing a third concept). The brand-analyzer 130analyzes and correlates the first, second, and third signatures todetermine the context of all the respective multimedia content elements.The context represents the sentiment of the brand.

In should be understood that the brand-analyzer 130 generates at leastone signature for each identified comment, thus a plurality of commentsmay represent a plurality of concepts. Next, a correlation between theconcepts is identified to determine, for example, if the sentiment ofthe brand is positive or negative, natural, popular, and so on. If thebrand-analyzer 130 identifies a large number of comments mentionedrespective of a certain brand, this may indicate that the brand is verypopular. A sentiment of the brand's popularity can be positive, naturalor negative depending, for example, on the content of the identifiedcomments and the signatures generated thereof, the context in which thecomments are made, and so on.

It should be further noted that using signatures for determining thecontext and thereby for the searching of advertisements ensures moreaccurate reorganization of multimedia content than, for example, whenusing metadata. For instance, in order to provide a matchingadvertisement for a sports car it may be desirable to locate a car of aparticular model. However, in most cases the model of the car would notbe part of the metadata associated with the multimedia content (image).Moreover, the car shown in an image may be at angles different from theangles of a specific photograph of the car that is available as a searchitem. The signature generated for that image would enable accuraterecognition of the model of the car because the signatures generated forthe multimedia content elements, according to the disclosed embodiments,allow for recognition and classification of multimedia content elements,such as, content-tracking, video filtering, multimedia taxonomygeneration, video fingerprinting, speech-to-text, audio classification,element recognition, video/image search and any other applicationrequiring content-based signatures generation and matching for largecontent volumes such as, web and other large-scale databases.

The signatures generated for more than one multimedia content elementthat relate to the brand are clustered. The clustered signatures areused to determine the context, and thereby the sentiment of the brand.The sentiment determined to the brand is saved in the data warehouse 160(or any other database that may be connected to the brand-analyzer 130).The trendiness of a brand's sentiment is determined respective ofpreviously determined sentiments as stored in the database. For example,the sentiment of the brand may trend from a positive to a negativesentiment over time, or vice versa.

FIG. 2 depicts an exemplary and non-limiting flowchart 200 describingthe process of matching an advertisement to multimedia content displayedon a web-page. At S205, the method starts when a web-page is uploaded toone of the web-browsers (e.g., web browser 120-1). In S210, a request tomatch at least one multimedia content element contained in the uploadedweb-page of an appropriate advertisement item is received. The requestcan be received from a publisher server, a script running on theuploaded web-page, or an agent (e.g., an add-on) installed in the webbrowser. S210 can also include extracting the multimedia contentelements for a signature that should be generated.

In S220, at least one signature for the multimedia content elementexecuted from the web page is generated. The signature for themultimedia content element generated by a signature generator isdescribed below with respect to FIGS. 3 and 4. In one embodiment, basedon the generated signatures, the context of the multimedia contentelements related to a trend, and thereby the brand sentiment, aredetermined as described below with reference to FIG. 5.

In S230, an advertisement item is matched to the multimedia contentelement respective of its generated signatures and/or the determinedcontext. According to one embodiment, the matching process includessearching for at least one advertisement item respective of thesignature of the multimedia content and a display of the at least oneadvertisement item within the display area of the web-page. According toanother embodiment, the signatures generated for the multimedia contentelements are clustered and the cluster of signatures is matched to oneor more advertisement items. According to yet another embodiment, thematching of an advertisement to a multimedia content element can beperformed by the computational cores that are part of a large scalematching discussed in detail below.

In S240, upon a users gesture the advertisement item is uploaded to theweb-page and displayed therein. The user's gesture may be: a scroll onthe multimedia content element, a press on the multimedia contentelement, and/or a response to the multimedia content. This ensures thatthe user's attention is given to the advertised content. In S250, it ischecked whether there are additional requests to analyze multimediacontent elements, and if so, execution continues with S210; otherwise,execution terminates.

As a non-limiting example for the operation of the process shown in FIG.2, a user uploads a web-page that contains an image of a sea shore. Theimage is then analyzed and a signature is generated respective thereto.Respective of the image signature, an advertisement item (e.g., abanner) is matched to the image, for example, a swimsuit advertisement.Upon detection of a user's gesture, for example, a mouse scrolling overthe sea shore image, the swimsuit ad is displayed.

FIGS. 3 and 4 illustrate the generation of signatures for the multimediacontent elements by the SGS 140 according to one embodiment. Anexemplary high-level description of the process for large scale matchingis depicted in FIG. 3. In this example, the matching is for a videocontent.

Video content segments 2 from a Master database (DB) 6 and a Target DB 1are processed in parallel by a large number of independent computationalCores 3 that constitute an architecture for generating the Signatures(hereinafter the “Architecture”). Further details on the computationalCores generation are provided below. The independent Cores 3 generate adatabase of Robust Signatures and Signatures 4 for Targetcontent-segments 5 and a database of Robust Signatures and Signatures 7for Master content-segments 8. An exemplary and non-limiting process ofsignature generation for an audio component is shown in detail in FIG.4. Finally, Target Robust Signatures and/or Signatures are effectivelymatched, by a matching algorithm 9, to Master Robust Signatures and/orSignatures database to find all matches between the two databases.

To demonstrate an example of the signature generation process, it isassumed, merely for the sake of simplicity and without limitation on thegenerality of the disclosed embodiments, that the signatures are basedon a single frame, leading to certain simplification of thecomputational cores generation. The Matching System is extensible forsignatures generation capturing the dynamics in-between the frames.

The Signatures' generation process is now described with reference toFIG. 4. The first step in the process of signatures generation from agiven speech-segment is to breakdown the speech-segment to K patches 14of random length P and random position within the speech segment 12. Thebreakdown is performed by the patch generator component 21. The value ofthe number of patches K, random length P and random position parametersis determined based on optimization, considering the tradeoff betweenaccuracy rate and the number of fast matches required in the flowprocess of the brand-analyzer 130 and SGS 140. Thereafter, all the Kpatches are injected in parallel into all computational Cores 3 togenerate K response vectors 22, which are fed into a signature generatorsystem 23 to produce a database of Robust Signatures and Signatures 4.

In order to generate Robust Signatures, i.e., Signatures that are robustto additive noise L (where L is an integer equal to or greater than 1)by the Computational Cores 3 a frame ‘i’ is injected into all the Cores3. Then, Cores 3 generate two binary response vectors: {right arrow over(S)} which is a Signature vector, and {right arrow over (RS)} which is aRobust Signature vector.

For generation of signatures robust to additive noise, such asWhite-Gaussian-Noise, scratch, etc., but not robust to distortions, suchas crop, shift and rotation, etc., a core Ci={ni} (1≦i≦L) may consist ofa single leaky integrate-to-threshold unit (LTU) node or more nodes. Thenode ni equations are:

$V_{i} = {\sum\limits_{j}\;{w_{ij}k_{j}}}$ ni = (Vi-Thx)

where, is a Heaviside step function; wij is a coupling node unit (CNU)between node i and image component j (for example, grayscale value of acertain pixel j); kj is an image component ‘j’ (for example, grayscalevalue of a certain pixel j); ThX is a constant Threshold value, where‘x’ is ‘S’ for Signature and ‘RS’ for Robust Signature; and Vi is aCoupling Node Value.

The Threshold values ThX are set differently for Signature generationand for Robust Signature generation. For example, for a certaindistribution of Vi values (for the set of nodes), the thresholds forSignature (ThS) and Robust Signature (ThRS) are set apart, afteroptimization, according to at least one or more of the followingcriteria:

-   -   1: For:        V_(i)>Th_(RS)        1−p(V>Th _(S))−1−(1−ε)^(l)<<1        i.e., given that I nodes (cores) constitute a Robust Signature        of a certain image I, the probability that not all of these I        nodes will belong to the Signature of same, but noisy image, Ĩ        is sufficiently low (according to a system's specified        accuracy).    -   2:        p(V _(i) >Th _(RS))≈l/L        i.e., approximately I out of the total L nodes can be found to        generate a Robust Signature according to the above definition.    -   3: Both Robust Signature and Signature are generated for certain        frame i.

It should be understood that the generation of a signature isunidirectional, and typically yields lossless compression, where thecharacteristics of the compressed data are maintained but theuncompressed data cannot be reconstructed. Therefore, a signature can beused for the purpose of comparison to another signature without the needof comparison to the original data. The detailed description of theSignature generation can be found in U.S. Pat. Nos. 8,326,775 and8,312,031, assigned to common assignee, which are hereby incorporated byreference for all the useful information they contain.

A Computational Core generation is a process of definition, selection,and tuning of the parameters of the cores for a certain realization in aspecific system and application. The process is based on several designconsiderations, such as:

-   -   (a) The Cores should be designed so as to obtain maximal        independence, i.e., the projection from a signal space should        generate a maximal pair-wise distance between any two cores'        projections into a high-dimensional space.    -   (b) The Cores should be optimally designed for the type of        signals, i.e., the Cores should be maximally sensitive to the        spatio-temporal structure of the injected signal, for example,        and in particular, sensitive to local correlations in time and        space. Thus, in some cases a core represents a dynamic system,        such as in state space, phase space, edge of chaos, etc., which        is uniquely used herein to exploit their maximal computational        power.    -   (c) The Cores should be optimally designed with regard to        invariance to a set of signal distortions, of interest in        relevant applications.

A detailed description of the Computational Core generation and theprocess for configuring such cores is discussed in more detail in theco-pending U.S. patent application Ser. No. 12/084,150 referenced above.

FIG. 5 is a non-limiting and exemplary flowchart 500 describing a methodfor determining a sentiment of a particular brand according to oneembodiment. In S510, a request to monitor a brand is received. Therequest may include at least one multimedia content element respectiveof the brand. A multimedia content element may be, for example, animage, a graphic, a video stream, a video clip, an audio stream, anaudio clip, a video frame, a photograph, an image of signals,combinations thereof, and portions thereof.

In S520, at least one signature is generated for each multimedia contentelement that is received. The at least one signature is robust to noiseand distortions and is generated by the SGS 140 as described in greaterdetail above. The brand in the received multimedia content element isidentified by the generated signature(s)

In S530, a search is performed for one or more multimedia contentelements in which the brand may be identified. According to oneembodiment, the search may be performed by crawling one or more websources and/or web platforms to identify the existence of multimediacontent elements that relate to the brand, for example, elements thatmention, show, and/or describe, the brand. As a non-limiting example,the crawling may be performed through social media networks, web sites,blogs, news feeds, multimedia channels, or any platform in which thebrand may be advertised and mentioned.

In S540, at least one signature is generated for each multimedia contentelement encountered during the search. Such signatures are also createdby the SGS 140. In S545, it is checked if at least one of the multimediacontent elements encountered during the search is related by referenceto the brand which requested to be monitored. According to oneembodiment, S545 includes comparing the at least one signature generatedin S520 to the least one signature generated in S540; if the signaturesare substantially similar, then it is determined that the brand ismentioned in the specific encountered multimedia element.

In S550, the signatures of those multimedia elements that are related tothe brand are marked. In S555, the context of the marked signatures isdetermined by correlating their respective concepts as discussed above.

In S560, the sentiment of the brand is determined using the context.According to one embodiment, S560 includes identification of a strongcontext with respect to a certain sentiment value (e.g., positive,natural, or negative) by checking if a predefined number of conceptssatisfy the same predefined condition. The predefined condition is setrespective of a certain sentiment value. For example, if 70% of theconcepts can be considered as trending towards a positive sentiment,then a strong context is established. A strong context can be alsoestablished based on the volume of the appearances of the brand in thecrawled sources, that is, if the total number of related conceptsexceeds a predefined threshold.

According to one embodiment the sentiment of the brand can be determinedby correlating the concept generated for the brand with other conceptsto determine if the brand has a positive, natural or negativeconnotation with respect to the other concept. The correlation can beperformed using probabilistic models. According to another embodimentthe trendiness of a brand's sentiment is also determined based onpreviously determined sentiments as stored in the database.

In S565, the determined sentiment for the brand is saved in a database(e.g., data warehouse 160). According one embodiment, the determinedsentiment is saved in an entry that also maintains the brand name, themarked signatures (S550), and a time stamp. In S570, it is checkedwhether there are additional requests and if so execution continues withS510; otherwise, execution terminates.

In one embodiment, the trendiness is determined by evaluating changes inthe sentiment values of a specific brand over time by analyzing thesentiment values and their time stamps as recorded in the database.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the invention and the concepts contributed by the inventor tofurthering the art, and are to be construed as being without limitationto such specifically recited examples and conditions. Moreover, allstatements herein reciting principles, aspects, and embodiments of theinvention, as well as specific examples thereof, are intended toencompass both structural and functional equivalents thereof.Additionally, it is intended that such equivalents include bothcurrently known equivalents as well as equivalents developed in thefuture, i.e., any elements developed that perform the same function,regardless of structure.

What is claimed is:
 1. A method for matching an advertisement item tomultimedia content elements based on sentiments, comprising: extractingat least one multimedia content element from a web-page requested fordisplay on a user node; generating a signature for each of the at leastone multimedia content element, wherein each signature represents aconcept, wherein each concept is an abstract description of one of theat least one multimedia content element; correlating the concepts of thegenerated signatures to determine a context of the at least onemultimedia content element, wherein the context indicates at least abrand sentiment; searching for at least one advertisement item based onthe signatures and the context; and causing a display of the at leastone advertisement item within a display area of the web-page.
 2. Themethod of claim 1, further comprising: storing the context, a timestamp, and the generated signatures in a database.
 3. The method ofclaim 1, wherein correlating the concepts of the generated signatures todetermine a context of the at least one multimedia content elementfurther comprises: establishing a strong context, wherein the brandsentiment is set based on the strong context.
 4. The method of claim 3,wherein the strong context is established based on at least one of: avolume of appearances of the brand throughout a plurality of web sourcesincluding a plurality of multimedia content elements, and a number ofconcepts satisfying a predefined condition being set respective of aparticular brand sentiment.
 5. The method of claim 4, wherein theparticular brand sentiment is any of: a positive sentiment, a neutralsentiment, and a negative sentiment.
 6. The method of claim 5, whereinthe displayed at least one advertisement is associated with a positivesentiment.
 7. The method of claim 1, wherein the at least oneadvertisement item is displayed when a user gesture of a user isdetected by a user node.
 8. The method of claim 7, wherein the usergesture is any of: a scroll on the media content, a press on the mediacontent, and a response to the media content.
 9. The method of claim 1,further comprising: clustering the generated signatures into a signaturecluster, wherein the at least one advertisement is searched for basedfurther on the signature cluster.
 10. A non-transitory computer readablemedium having stored thereon instructions for causing one or moreprocessing units to execute the method according to claim
 1. 11. Asystem for matching an advertisement item to a multimedia contentelement based on sentiments, comprising: a processing unit; and amemory, the memory containing instructions that, when executed by theprocessing unit, configures the system to: extract at least onemultimedia content element from a web-page requested for display on auser node; generate a signature for each of the at least one multimediacontent element, wherein each signature represents a concept, whereineach concept is an abstract description of one of the at least onemultimedia content element; correlate the concepts of the generatedsignatures to determine a context of the at least one multimedia contentelement, wherein the context indicates at least a brand sentiment;search for at least one advertisement item based on the signatures andthe context; and cause a display of the at least one advertisement itemwithin a display area of the web-page.
 12. The system of claim 11,wherein the system is further configured to: store the brand sentiment,a time stamp, and the generated signatures in a database.
 13. The systemof claim 11, wherein the system is further configured to: establish astrong context, wherein the brand sentiment is set based on the strongcontext.
 14. The system of claim 13, wherein the strong context isestablished based on at least one of: a volume of appearances of thebrand throughout a plurality of web sources including a plurality ofmultimedia content elements, and a number of concepts satisfying apredefined condition being set respective of a particular brandsentiment.
 15. The system of claim 14, wherein the particular brandsentiment is any of: a positive sentiment, a neutral sentiment, and anegative sentiment.
 16. The system of claim 15, wherein the displayed atleast one advertisement is associated with a positive sentiment.
 17. Thesystem of claim 11, wherein the at least one advertisement item isdisplayed when a user gesture of a user is detected by a user node. 18.The system of claim 17, wherein the user gesture is any of: a scroll onthe media content, a press on the media content, and a response to themedia content.
 19. The system of claim 11, wherein the system is furtherconfigured to: cluster the generated signatures into a signaturecluster, wherein the at least one advertisement is searched for furtherbased on the signature cluster.